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Author Notes:

Correspondence to: Kyle Steenland, Emory University, Rollins School Public Health, 1518 Clifton Road, Atlanta, GA 30322, USA. nsteenl@emory.edu.


Research Funding:

Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12–2-0012).

ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics.

The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada.

Private sector contributions are facilitated by the Foundation for the National Institutes of Health.

The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California.

ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

This work was also supported by an NIH Grant for the Emory Alzheimer’s Disease Research Center (P50 AG025688).

Alvaro Alonso is supported by grant U01HL096902.


  • Science & Technology
  • Life Sciences & Biomedicine
  • Neurosciences
  • Neurosciences & Neurology
  • Alzheimer's disease
  • biomarkers
  • cerebrospinal fluid
  • dementia
  • imaging
  • mild cognitive impairment

A 'Framingham-like' Algorithm for Predicting 4-Year Risk of Progression to Amnestic Mild Cognitive Impairment or Alzheimer's Disease Using Multidomain Information


Journal Title:

Journal of Alzheimer's Disease


Volume 63, Number 4


, Pages 1383-1393

Type of Work:

Article | Post-print: After Peer Review


Background: There are no agreed-upon variables for predicting progression from unimpaired cognition to amnestic mild cognitive impairment (aMCI), or from aMCI to Alzheimer's disease (AD). Objective: Use ADNI data to develop a 'Framingham-like' prediction model for a 4-year period. Methods: We developed models using the strongest baseline predictors from six domains (demographics, neuroimaging, CSF biomarkers, genetics, cognitive tests, and functional ability). We chose the best predictor from each domain, which was dichotomized into more versus less harmful. Results: There were 224 unimpaired individuals and 424 aMCI subjects with baseline data on all predictors, of whom 37 (17%) and 150 (35%) converted to aMCI and AD, respectively, during 4 years of follow-up. For the unimpaired, CSF tau/Aβ ratio, hippocampal volume, and a memory score predicted progression. For those aMCI at baseline, the same predictors plus APOE4 status and functional ability predicted progression. Demographics and family history were not important predictors for progression for either group. The fit statistic was good for the unimpaired-aMCI model (C-statistic 0.80) and very good for the aMCI-AD model (C-statistic 0.91). Among the unimpaired, those with no harmful risk factors had a 4-year predicted 2% risk of progression, while those with the most harmful risk factors had a predicted 35% risk. The aMCI subjects with no harmful risk factors had a predicted 1% risk of progression those with all six harmful risk factors had a predicted 90% risk. Conclusion: Our parsimonious model accurately predicted progression from unimpaired to aMCI with three variables, and from aMCI to AD with five variables.

Copyright information:

© 2018 - IOS Press and the authors. All rights reserved.

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